Work

Assay of Metal Loss in Pipelines With Repaired Sleeves Using Machine-Learning-Assisted Fiber-Optic Distributed Acoustic Sensing

Year: 2024

Type: article

Source: IEEE Sensors Journal

DOI: 10.1109/jsen.2024.3513433

Authors: Juan M. Marin, Islam Ashry, Abderrahim Fakiri, Alaaeddine Rjeb, Shaj K. Manjalivalapil, Chun Hong Kang, Tien Khee Ng, Boon S. Ooi

Institutions: King Abdullah University of Science and Technology, Saudi Aramco (Saudi Arabia), Saudi Aramco (United States)

Language: English

Topic: Advanced Fiber Optic Sensors

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: closed

Funders: King Abdullah University of Science and Technology, Saudi Aramco

Awards: G1005952190, G133275875

OpenAlex ID: W4405360844

FWCI: 0.617

Cites: 50

Cited by: 6

Related to: 10